RESEARCH PAPER
Hybrid Metaheuristic Optimisation Algorithms with Least-Squares Support Vector Machine and stagnation counter for prediction vibration induced by Tunnel blasting
1
.State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China;, China
2
School of Civil Engineering, Sun Yat-Sen University (State Key Laboratory for Tunnel Engineering (Sun Yat-sen University)), Zhuhai, 519082, China, China
3
CCCC Second Harbor Engineering Company Ltd., Wuhan, 430040, China
Submission date: 2025-09-30
Final revision date: 2025-11-13
Acceptance date: 2026-02-14
Online publication date: 2026-02-23
Corresponding author
Yingkang Yao
.State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China;, China
.State Key Laboratory of Precision Blasting, Jianghan University, Wuhan 430056, China;, China
KEYWORDS
least squares support vector machinetunnel blastingvibration predictionstall counterwhale optimisation algorithm
TOPICS
ABSTRACT
Ground vibrations induced by tunnel blasting can severely impact nearby infrastructure. Therefore, accurate prediction of peak particle velocity (PPV) is essential for ensuring structural safety and engineering sustainability. This study proposes a PPV prediction model based on the Least Squares Support Vector Machine (LSSVM), optimised by a novel Adaptive Stagnation Whale Optimisation Algorithm (ASWOA) . To address the limitations of the conventional WOA, a regionally dynamic threshold adjustment strategy based on stagnation counter is proposed. recording the number of consecutive iterations without improvement, and calculate the dynamic threshold by combining the decay coefficient to control the rate of change, thereby adaptively adjusts the trigger probability of spiral updates, improving global search capability. Compared with others models, the proposed method not only improves prediction accuracy but also ensure higher reliability in vibration prediction. Moreover, it provides an efficient tool for vibration control in tunnel blasting under complex geological conditions.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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